Time-of-flight systems have been used to calculate a distance of an object based on an amount of time it is takes a pulse of light to travel from a transmitters to the object and then from the object to a light detector. Different time-of-flight systems have been used for different applications.
For example, in golf, time-of-flight range finders have been used to calculate a distance to the hole. These range finders have typically been designed as linear systems that output a narrow, straight line laser beam. Once the laser beam struck an object in the straight line path, a reflected portion of the laser beam striking the object was detected at a detector and the distance of the object was calculated. These range finders could only measure the distance of the first object in the laser beam path; the range finders could not differentiate between different objects at different distances nor could they identify the distance of objects in more than one dimension.
More sophisticated time-of-flight devices included image sensors, such as those in digital cameras, containing an array of many light detection cavities or photosites. The image sensors were capable of measuring the distance and position of multiple objects in at least two dimensions based on the detected location of the light reflected off each object within the array and the calculated time-of-flight. However, image sensors are expensive and slow. Image sensors require relatively long processing times to analyze the data at each of the photosites in the array. Additionally, while the accuracy of these devices improves as the number of photosites increases, the cost and processing time need to analyze the data at each of the photosites also increases. This makes the use of image sensors impractical for low cost or time sensitive applications, such as vehicle crash avoidance systems.
There is therefore a need for quickly and accurately calculating a position of multiple objects in at least two dimensions in a cost effective manner.